Research

Research, evidence gathering, literature, reports, investigation, and synthesis

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Interlayer Information Similarity Assessment Of Deep Neural Networks Via Topological Similarity And Persistence Analysis Of Data Neighbour DynamicsA**arXiv ID:** 2012.03793 **Authors:** Andrew Hryniowski, Alexander Wong **Published:** 2020-12-07T15:34:58Z **Abstract:** The quantitative analysis of information structure through a deep neural network (DNN) can unveil new insights into the theoretical performance of DNN architectures. Two very promising avenues of research towards quantitative information structure analysis are: 1) layer similarity (LS) strategies focused on the inter-layer feature similarity, and 2) intrinsic dimensionalit...researchperformanceVotes: 0GitHub stars: 3
Incorporating Symbolic Domain Knowledge Into Graph Neural NetworksA**arXiv ID:** 2010.13900 **Authors:** Tirtharaj Dash, Ashwin Srinivasan, Lovekesh Vig **Published:** 2020-10-23T16:22:21Z **Abstract:** Our interest is in scientific problems with the following characteristics: (1) Data are naturally represented as graphs; (2) The amount of data available is typically small; and (3) There is significant domain-knowledge, usually expressed in some symbolic form. These kinds of problems have been addressed effectively in the past by Inductive Logic Programming ...researchexpressperformanceVotes: 0GitHub stars: 3
Higher Gauge Theory CohomologyAHigher Gauge Theory via Differential Nonabelian Cohomology — streamlined introduction to global completion of Maxwell-type higher gauge fields using cohesive homotopy theory and flux quantization.researchVotes: 0GitHub stars: 3
Graphical Coaction Frw IntegralsAGraphical coaction methodology for FRW integrals using twisted (co)homology intersection theory — decomposing cosmological integrals into diagram-decorated building blocks.researchgoVotes: 0GitHub stars: 3
From Read Out Geometry To In Silico StimulationADistributed functional-connectivity signature of Alzheimer's disease methodology using subject-specific reservoir-computing models to reconstruct individual lagged functional connectivity and develop personalized neuromodulation strategies. Shows that optimal stimulation targets are distributed patterns rather than focal sites, requiring model-informed targeting based on therapeutic responsiveness rather than read-out deviation magnitude.researchpythonnodeVotes: 0GitHub stars: 3
Floquet Controlled Phonon LasingAFloquet-engineered phonon lasing methodology for quantum control systems. Design squeezed phonon lasers via Floquet control of solid-state defects with coupled mechanical oscillators and spin systems. From arXiv:2606.05083 (Molinares, Rastelli, Montenegro, Eremeev, 2026).researchgoVotes: 0GitHub stars: 3
Fermionic Bell Sampling Non GaussianityAFermionic non-Gaussianity analysis via Bell sampling — bridge degree monotone, Gaussian conversion no-go theorems, and efficient quantum algorithms for certifying non-Gaussian cost of state preparation.researchgoVotes: 0GitHub stars: 3
Faster Physics In PythonASkill for AI agent capabilitiesresearchpythongoVotes: 0GitHub stars: 3
Exclusion Statistics Thermodynamic ResourceAHaldane fractional exclusion statistics as tunable thermodynamic resource for quantum heat engines — bosonic working mediums exceed fermionic Whitney power limit by 1.52×.researchgoperformanceVotes: 0GitHub stars: 3
Entropy Maximization ManifoldAMaximum entropy path ensemble embedding for manifold learning and dimensionality reductionresearchgoVotes: 0GitHub stars: 3
Degree Tensor Train VarietiesAIntegral geometry methodology for computing degrees of tensor train varieties.researchgoVotes: 0GitHub stars: 3
Cross Modal ConvergenceACross-modal representational convergence methodology using Generalized Procrustes Algorithm to measure intra-modal dispersion at single-stimulus level. Low intra-modal dispersion predicts up to 2× higher cross-modal alignment. Activation: representational similarity, RSA, Procrustes, cross-modal, model-brain alignment, vision-language, DINOv2.researchpythonrustVotes: 0GitHub stars: 3
Cross Modal Convergence ModulationAModulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion - neural network methodology for modulating representational convergence across architectures and modalities. Reveals how single-stimulus dispersion within modalities predicts cross-modal alignment. Based on arXiv:2604.21836.researchpythongoVotes: 0GitHub stars: 3
Classical Disjunction Effect ModelAClassical probability model that reproduces the disjunction effect via expectation-parameter partitioning, proving classical and quantum-like models have equivalent expressiveness for decision rates.researchpythonexpressVotes: 0GitHub stars: 3
Carleman Linearization Ode SolverACarleman linearization methodology for converting nonlinear ODEs into infinite-dimensional linear systems via tensor powers. C2 (2nd order truncation) recovers both transient and steady-state solutions. Use when: (1) solving nonlinear differential equations, (2) quantum algorithms for ODEs (HHL-based), (3) fluid dynamics steady-state approximation, (4) converting nonlinear systems to linear form for quantum computation, (5) numerical analysis of dynamical systems. Keywords: carleman lineariza...researchgoangularVotes: 0GitHub stars: 3
Automated Entropy Inequality ProvingAAutomated proving methodology for Shannon-type entropy inequalities using fine-tuned language models and guided tree search. Bridges information theory with AI for automated mathematical theorem discovery. Applicable to entropy inequality verification, information theory research, and automated theorem proving. arXiv: 2606.05729.researchrustgoVotes: 0GitHub stars: 3
Attack Detection Time Series Foundation ModelsAModel-structure-free attack detection for cyber-physical systems using TimesFM time-series foundation model as surrogate residual generator. Zero-shot detection without plant model knowledge, handling both model-free replay attacks and model-based stealthy attacks. Foundation models as corrupted measurement substitutes when redundancy assumptions fail. Applications: CPS security, power systems, network attack detection. Activation: attack detection, cyber-physical security, TimesFM, foundatio...researchpythongoVotes: 0GitHub stars: 3
Arxiv 2608 20009 Exphy A Benchmark For Explicit Physical Property LAExPhy: A Benchmark for Explicit Physical Property Learning in Multi-Object Trajectory Forecasting (arXiv: 2608.20009)researchgogitVotes: 0GitHub stars: 3
Phinn Eeg Topological Dream AnalysisATopological time-series (TDA) framework for EEG analysis — sliding-window Takens delay embeddings + Vietoris-Rips filtrations to extract Dynamic Betti Curves, then topology-conditioned flow matching / rectified flow for neural signal synthesis and rare-event (dream-state) detection. Use when analyzing multichannel EEG/EMG time series where spectral-energy features (PSD, catch22) plateau, when building EEG foundation/synthesis models, or when studying dream-state / consciousness / sleep neural...researchpythongoVotes: 0GitHub stars: 3
Persistent Homology Brain Network ControlAPersistent homology methodology for brain network control that broadens the controllable subspace in human structural connectomes. Uses topological cycles to identify driver nodes beyond traditional degree-based selection, revealing dissociation between control cost and control geometry. Use when analyzing brain network controllability, structural connectomes, or topological neuroscience applications.researchgonodeVotes: 0GitHub stars: 3
Worldcuparena Fine Grained Evaluation Of LanguageADerived from arXiv:2607.18084 - WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football ForecastingresearchVotes: 0GitHub stars: 3
Wigner Function ReconstructionAWigner function reconstruction methodology for continuous-variable quantum system characterization. Combines provably efficient regression for sparse states (binomial codes, cat states) with deep learning for general states (GKP). Use when: (1) characterizing CV quantum systems, (2) reconstructing Wigner functions from sparse measurements, (3) identifying error processes in QEC cycles, (4) phase-space tomography, (5) reducing measurement overhead in quantum state characterization. Trigger wor...researchgoVotes: 0GitHub stars: 3
Why Classic Transformers Are Shallow And How To Make Them Go DeepA**arXiv ID:** 2312.06182 **Authors:** Yueyao Yu, Yin Zhang **Published:** 2023-12-11T07:49:16Z **Abstract:** Since its introduction in 2017, Transformer has emerged as the leading neural network architecture, catalyzing revolutionary advancements in many AI disciplines. The key innovation in Transformer is a Self-Attention (SA) mechanism designed to capture contextual information. However, extending the original Transformer design to models of greater depth has proven exceedingly challenging,...researchgoVotes: 0GitHub stars: 3
When To Smell In StereoAStereo olfaction utility analysis framework - determines when dual nostril 'stereo' olfaction provides advantages over single nostril 'mono' olfaction based on odor concentration gradients and spatial correlation length scales. Use when analyzing animal olfactory navigation, odor trail tracking, or surface-based olfactory search strategies.researchexpressrailsVotes: 0GitHub stars: 3